Reactive Users vs. Recommendation Systems: An Adaptive Policy to Manage Opinion Drifts

2025-08-18

AI Summary

Mainstream recommender systems often induce opinion drift in users, undermining viewpoint stability. Method: This paper investigates whether reactive users can mitigate such algorithm-induced opinion shifts through adaptive content selection. We propose an adaptive consumption strategy grounded in dynamically adjusted click probabilities, modeling users as rational agents capable of perceiving and actively counteracting opinion change. Contribution/Results: Theoretical analysis and numerical simulations demonstrate that the strategy significantly reduces expected opinion drift magnitude; when opinion retention weight is high, it improves users’ expected utility relative to fixed-click baselines. Crucially, this work provides the first formal characterization of users’ capacity to exert countervailing influence against algorithmic bias, and proves its efficacy within dynamic opinion evolution models.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Multiagent Systems: Adversarial Agents
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Recommendation systems are used in a range of platforms to maximize user engagement through personalization and the promotion of popular content. It has been found that such recommendations may shape users' opinions over time. In this paper, we ask whether reactive users, who are cognizant of the influence of the content they consume, can prevent such changes by adaptively adjusting their content consumption choices. To this end, we study users' opinion dynamics under two types of stochastic policies: a passive policy where the probability of clicking on recommended content is fixed and a reactive policy where clicking probability adaptively decreases following large opinion drifts. We analytically derive the expected opinion and user utility under these policies. We show that the adaptive policy can help users prevent opinion drifts and that when a user prioritizes opinion preservation, the expected utility of the adaptive policy outperforms the fixed policy. We validate our theoretical findings through numerical simulations. These findings help better understand how user-level strategies can challenge the biases induced by recommendation systems.

Problem

• Studying how reactive users prevent opinion drifts from recommendations
• Analyzing adaptive vs fixed policies for opinion preservation
• Evaluating user utility under recommendation system influence

Innovation

• Adaptive policy adjusts user clicking probability
• Stochastic policies manage opinion drift dynamics
• Reactive users prevent recommendation system biases